Offset and loaded weights differ by one

[Ajaz-Ahmad]

Hi,

I was inspired by your code on loading weights for tiny yolo v2 from coco weight file. I have implemented my yolo network as below:

However, there is a difference in the final offset and loaded weights of 1. I am not able to figure out what mistake I am making. Could you please help me out?
Here is more details:

Size of weight file is -> 44948600
Total number of params to load = 11237146
Loading 496 weights of conv0 ...
Loading 4736 weights of conv1 ...
Loading 18688 weights of conv2 ...
Loading 74240 weights of conv3 ...
Loading 295936 weights of conv4 ...
Loading 1181696 weights of conv5 ...
Loading 4722688 weights of conv6 ...
Loading 4720640 weights of conv7 ...
Loading 218025 weights of conv8 ...
len-> 11237145
len-> 11237146
Final offset = 11237145
Total number of params in the weight file = 11237146

Name of All Variables:
All Var [<tf.Variable 'conv0/kernel:0' shape=(3, 3, 3, 16) dtype=float32_ref>, <tf.Variable 'conv0_bn/gamma:0' shape=(16,) dtype=float32_ref>, <tf.Variable 'conv0_bn/beta:0' shape=(16,) dtype=float32_ref>, <tf.Variable 'conv0_bn/moving_mean:0' shape=(16,) dtype=float32_ref>, <tf.Variable 'conv0_bn/moving_variance:0' shape=(16,) dtype=float32_ref>, <tf.Variable 'conv1/kernel:0' shape=(3, 3, 16, 32) dtype=float32_ref>, <tf.Variable 'conv1_bn/gamma:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv1_bn/beta:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv1_bn/moving_mean:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv1_bn/moving_variance:0' shape=(32,) dtype=float32_ref>, <tf.Variable 'conv2/kernel:0' shape=(3, 3, 32, 64) dtype=float32_ref>, <tf.Variable 'conv2_bn/gamma:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv2_bn/beta:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv2_bn/moving_mean:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv2_bn/moving_variance:0' shape=(64,) dtype=float32_ref>, <tf.Variable 'conv3_1/kernel:0' shape=(3, 3, 64, 128) dtype=float32_ref>, <tf.Variable 'conv3_1_bn/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv3_1_bn/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv3_1_bn/moving_mean:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv3_1_bn/moving_variance:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'conv4_1/kernel:0' shape=(3, 3, 128, 256) dtype=float32_ref>, <tf.Variable 'conv4_1_bn/gamma:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv4_1_bn/beta:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv4_1_bn/moving_mean:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv4_1_bn/moving_variance:0' shape=(256,) dtype=float32_ref>, <tf.Variable 'conv5_1/kernel:0' shape=(3, 3, 256, 512) dtype=float32_ref>, <tf.Variable 'conv5_1_bn/gamma:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv5_1_bn/beta:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv5_1_bn/moving_mean:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv5_1_bn/moving_variance:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv6_1/kernel:0' shape=(3, 3, 512, 1024) dtype=float32_ref>, <tf.Variable 'conv6_1_bn/gamma:0' shape=(1024,) dtype=float32_ref>, <tf.Variable 'conv6_1_bn/beta:0' shape=(1024,) dtype=float32_ref>, <tf.Variable 'conv6_1_bn/moving_mean:0' shape=(1024,) dtype=float32_ref>, <tf.Variable 'conv6_1_bn/moving_variance:0' shape=(1024,) dtype=float32_ref>, <tf.Variable 'conv6_3/kernel:0' shape=(3, 3, 1024, 512) dtype=float32_ref>, <tf.Variable 'conv6_3_bn/gamma:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv6_3_bn/beta:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv6_3_bn/moving_mean:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv6_3_bn/moving_variance:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'conv_dec/kernel:0' shape=(1, 1, 512, 425) dtype=float32_ref>, <tf.Variable 'conv_dec/bias:0' shape=(425,) dtype=float32_ref>]
Thanks

[Ajaz-Ahmad]

resolved it.